{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential Model Demo Full CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 00. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyCNN\n"
     ]
    }
   ],
   "source": [
    "print(\"MyCNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.activation.relu import ReluLayer\n",
    "from src.layers.pooling import MaxPoolLayer\n",
    "from src.activation.softmax import SoftmaxLayer\n",
    "from src.layers.dense import DenseLayer\n",
    "from src.layers.flatten import FlattenLayer\n",
    "from src.layers.convolutional import ConvLayer2D, SuperFastConvLayer2D\n",
    "from src.layers.dropout import DropoutLayer\n",
    "from src.model.sequential import SequentialModel\n",
    "from src.utils.core import convert_categorical2one_hot, convert_prob2categorical\n",
    "from src.utils.metrics import softmax_accuracy\n",
    "from src.optimizers.gradient_descent import GradientDescent\n",
    "from src.optimizers.rms_prop import RMSProp\n",
    "from src.optimizers.adam import Adam\n",
    "from src.utils.plots import lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01. Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of samples in the train data set\n",
    "N_TRAIN_SAMPLES = 50000\n",
    "# number of samples in the test data set\n",
    "N_TEST_SAMPLES = 2500\n",
    "# number of samples in the validation data set\n",
    "N_VALID_SAMPLES = 250\n",
    "# number of classes\n",
    "N_CLASSES = 10\n",
    "# image size\n",
    "IMAGE_SIZE = 28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02. Build data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape: (60000, 28, 28)\n",
      "trainY shape: (60000,)\n",
      "testX shape: (10000, 28, 28)\n",
      "testY shape: (10000,)\n"
     ]
    }
   ],
   "source": [
    "((trainX, trainY), (testX, testY)) = fashion_mnist.load_data()\n",
    "print(\"trainX shape:\", trainX.shape)\n",
    "print(\"trainY shape:\", trainY.shape)\n",
    "print(\"testX shape:\", testX.shape)\n",
    "print(\"testY shape:\", testY.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = trainX[:N_TRAIN_SAMPLES, :, :]\n",
    "y_train = trainY[:N_TRAIN_SAMPLES]\n",
    "\n",
    "X_test = trainX[N_TRAIN_SAMPLES:N_TRAIN_SAMPLES+N_TEST_SAMPLES, :, :]\n",
    "y_test = trainY[N_TRAIN_SAMPLES:N_TRAIN_SAMPLES+N_TEST_SAMPLES]\n",
    "\n",
    "X_valid = testX[:N_VALID_SAMPLES, :, :]\n",
    "y_valid = testY[:N_VALID_SAMPLES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** We need to change the data format to the shape supported by my implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (50000, 28, 28, 1)\n",
      "y_train shape: (50000, 10)\n",
      "X_test shape: (2500, 28, 28, 1)\n",
      "y_test shape: (2500, 10)\n",
      "X_valid shape: (250, 28, 28, 1)\n",
      "y_valid shape: (250, 10)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train / 255\n",
    "X_train = np.expand_dims(X_train, axis=3)\n",
    "y_train = convert_categorical2one_hot(y_train)\n",
    "X_test = X_test / 255\n",
    "X_test = np.expand_dims(X_test, axis=3)\n",
    "y_test = convert_categorical2one_hot(y_test)\n",
    "X_valid = X_valid / 255\n",
    "X_valid = np.expand_dims(X_valid, axis=3)\n",
    "y_valid = convert_categorical2one_hot(y_valid)\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)\n",
    "print(\"X_valid shape:\", X_valid.shape)\n",
    "print(\"y_valid shape:\", y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 03. Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = [\n",
    "    # input (N, 28, 28, 1) out (N, 28, 28, 32)\n",
    "    SuperFastConvLayer2D.initialize(filters=32, kernel_shape=(3, 3, 1), stride=1, padding=\"same\"),\n",
    "    # input (N, 28, 28, 32) out (N, 28, 28, 32)\n",
    "    ReluLayer(),\n",
    "    # input (N, 28, 28, 32) out (N, 28, 28, 32)\n",
    "    SuperFastConvLayer2D.initialize(filters=32, kernel_shape=(3, 3, 32), stride=1, padding=\"same\"),\n",
    "    # input (N, 28, 28, 32) out (N, 28, 28, 32)\n",
    "    ReluLayer(),\n",
    "    # input (N, 28, 28, 32) out (N, 14, 14, 32)\n",
    "    MaxPoolLayer(pool_size=(2, 2), stride=2),\n",
    "    # input (N, 14, 14, 32) out (N, 14, 14, 32)\n",
    "    DropoutLayer(keep_prob=0.75),\n",
    "    # input (N, 14, 14, 32) out (N, 14, 14, 64)\n",
    "    SuperFastConvLayer2D.initialize(filters=64, kernel_shape=(3, 3, 32), stride=1, padding=\"same\"),\n",
    "    # input (N, 14, 14, 64) out (N, 14, 14, 64)\n",
    "    ReluLayer(),\n",
    "    # input (N, 14, 14, 64) out (N, 14, 14, 64)\n",
    "    SuperFastConvLayer2D.initialize(filters=64, kernel_shape=(3, 3, 64), stride=1, padding=\"same\"),\n",
    "    # input (N, 14, 14, 64) out (N, 14, 14, 64)\n",
    "    ReluLayer(),\n",
    "    # input (N, 14, 14, 64) out (N, 7, 7, 64)\n",
    "    MaxPoolLayer(pool_size=(2, 2), stride=2),\n",
    "    # input (N, 7, 7, 64) out (N, 7, 7, 64)\n",
    "    DropoutLayer(keep_prob=0.75),\n",
    "    # input (N, 7, 7, 64) out (N, 7 * 7 * 64)\n",
    "    FlattenLayer(),\n",
    "    # input (N, 7 * 7 * 64) out (N, 256)\n",
    "    DenseLayer.initialize(units_prev=7 * 7 * 64, units_curr=256),\n",
    "    # input (N, 256) out (N, 256)\n",
    "    ReluLayer(),\n",
    "     # input (N, 256) out (N, 32)\n",
    "    DenseLayer.initialize(units_prev=256, units_curr=32),\n",
    "     # input (N, 32) out (N, 32)\n",
    "    ReluLayer(),\n",
    "     # input (N, 32) out (N, 10)\n",
    "    DenseLayer.initialize(units_prev=32, units_curr=N_CLASSES),\n",
    "     # input (N, 10) out (N, 10)\n",
    "    SoftmaxLayer()\n",
    "]\n",
    "\n",
    "optimizer = Adam(lr=0.003)\n",
    "\n",
    "model = SequentialModel(\n",
    "    layers=layers,\n",
    "    optimizer=optimizer\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 04. Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 00001 | test loss: 2.30312 | test accuracy: 0.10600 | time: 00:34:37.99\n",
      "iter: 00002 | test loss: 2.30310 | test accuracy: 0.08760 | time: 00:33:59.76\n",
      "iter: 00003 | test loss: 2.30307 | test accuracy: 0.08760 | time: 00:33:58.11\n",
      "iter: 00004 | test loss: 2.30305 | test accuracy: 0.08760 | time: 00:34:30.44\n",
      "iter: 00005 | test loss: 2.30303 | test accuracy: 0.08760 | time: 00:34:25.41\n",
      "iter: 00006 | test loss: 2.30301 | test accuracy: 0.08760 | time: 00:34:08.73\n",
      "iter: 00007 | test loss: 2.30300 | test accuracy: 0.08760 | time: 00:34:29.02\n",
      "iter: 00008 | test loss: 2.30300 | test accuracy: 0.08760 | time: 00:34:28.20\n",
      "iter: 00009 | test loss: 2.30299 | test accuracy: 0.08760 | time: 00:34:53.67\n",
      "iter: 00010 | test loss: 2.30298 | test accuracy: 0.08760 | time: 00:34:38.79\n"
     ]
    }
   ],
   "source": [
    "model.train(\n",
    "    x_train=X_train, \n",
    "    y_train=y_train, \n",
    "    x_test=X_test, \n",
    "    y_test=y_test, \n",
    "    epochs=10,\n",
    "    bs=256,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05. Predict and examine results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines(\n",
    "    y_1=np.array(model.history[\"train_acc\"]),\n",
    "    y_2=np.array(model.history[\"test_acc\"]),\n",
    "    label_1=\"train\",\n",
    "    label_2=\"test\",\n",
    "    title=\"ACCURACY\",\n",
    "    fig_size=(16,10),\n",
    "    path=\"../viz/cnn_acc.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines(\n",
    "    y_1=np.array(model.history[\"train_loss\"]),\n",
    "    y_2=np.array(model.history[\"test_loss\"]),\n",
    "    label_1=\"train\",\n",
    "    label_2=\"test\",\n",
    "    title=\"LOSS\",\n",
    "    fig_size=(16,10),\n",
    "    path=\"../viz/cnn_loss.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc:  0.092\n"
     ]
    }
   ],
   "source": [
    "y_hat = model.predict(X_valid)\n",
    "acc = softmax_accuracy(y_hat, y_valid)\n",
    "print(\"acc: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
